Monday, October 26
9:40 am - 10:00 am


  • Xixi Li (Speaker) The University of Manchester


Authors: Xixi Li; Fotios Petropoulos; Yanfei Kang

Time series forecasting plays an increasingly important role in modern business decisions.

In today's data-rich environment, people often want to choose the optimal forecasting model for their data. However, identifying the optimal model often requires professional knowledge and experience, making accurate forecasting a challenging task. To mitigate the importance of model selection, we propose a simple and reliable algorithm and successfully improve forecasting performance. Specifically, we construct multiple time series with different sub-seasons from the original time series. These derived series highlight different sub-seasonal patterns of the original time series, making it possible for the forecasting methods to capture diverse patterns and components of the data. Subsequently, we make extrapolation forecasts for these multiple time series separately with classical statistical models (ETS or ARIMA). Finally, forecasts of these multiple time series are averaged together with equal weights. Whether in point or interval predictions, we evaluate our approach on the widely used competition datasets M1, M3, and M4 and it improves the forecasting performance in total horizon compared with the benchmarks. We also study which pattern of time series is more suitable for our method.

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